Structure Discovery in Nonparametric Regression through Compositional Kernel Search

Understand

Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art.

• We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels.
• We present a method for searching over this space of structures which mirrors the scientific discovery process.
• The learned structures can often decompose functions into interpretable components and enable long-range extrapolation on time-series datasets.